Linearized iterative least-squares (LIL): A parameter fitting algorithm for component separation in multifrequency CMB experiments such as Planck

Understand

We present an efficient algorithm for the least squares parameter fitting optimized for component separation in multi-frequency CMB experiments.

• We sidestep some of the problems associated with non-linear optimization by taking advantage of the quasi-linear nature of the foreground model.
• We demonstrate our algorithm, linearized iterative least-squares (LIL), on the publicly available Planck sky model FFP6 simulations and compare our result with the other algorithms.
• We work at full Planck resolution and show that degrading the resolution of all channels to that of the lowest frequency channel is not necessary.